buildConfirmed22 publishers Anthropic launched Claude Haiku 5.5 at an average price about 75% below Haiku 4.5. The saving varies widely with prompt length, so teams moving classification, support or query traffic need to price their own requests before they switch models.
Perspective Coverage
22 publishers
- Builder
- Builder 47%
- Operator
- Operator 36%
- Investor
- Investor 17%
Reality
- Evidence66
- Adoption30
- Hype gap+25
- Incentives70
- Confidence65
buildOne report1 publisher H Company's Holo4 merges its desktop and API specialists into one open-weight model
H Company's open-weight Holo4 27B handles GUIs, code, MCP servers and REST APIs in one model, scoring 61.7% on OSWorld 2.0 at an estimated $1.22 per task. The choice between specialists happens during training, so agent teams could drop the router from their stack if the scores hold on their own workloads.
Reality
- Evidence35
- Adoption
- Not scored
- Hype gap+25
- Incentives
- Not scored
- Confidence40
OpenAI lists GPT-6.1 Sol at $2 per million input tokens and $10 per million output; the 10-cent figure in early coverage is its cached-input rate. Teams moving work off Astra should budget on the list rates and OpenAI's per-task costs.
Perspective Coverage
5 publishers
- Builder
- Builder 44%
- Operator
- Operator 34%
- Investor
- Investor 22%
Reality
- Evidence50
- Adoption
- Not scored
- Hype gap+35
- Incentives70
- Confidence60
OpenAI's GPT-6.1 Sol price cut rewards teams whose agents resend the same long context
OpenAI's GPT-6.1 Sol halves cached-input pricing to $0.10 per million tokens and leaves standard rates at $2 and $10. Agents that resend long context collect the saving, while other buyers weigh gains shown mostly in OpenAI's own tests.
Reality
- Evidence55
- Adoption30
- Hype gap+20
- Incentives65
- Confidence55
buildConfirmed8 publishers Google's third Flash release in six weeks keeps the $0.75/$3.75 rate card. But the model also spends more tokens per task. Both numbers in your cost model are moving before the price even changes.
Perspective Coverage
8 publishers
- Builder
- Builder 53%
- Operator
- Operator 29%
- Investor
- Investor 18%
Reality
- Evidence58
- Adoption35
- Hype gap+22
- Incentives72
- Confidence62
buildOne report1 publisher Astra bills at long-context rates once a request passes 30 percent of its input window
OpenAI's spec lets gpt-6-astra take 922,000 input tokens, but requests above 272,000 move to higher long-context rates. Provisioning a model that drives a desktop, a shell and MCP servers starts with that threshold.
Reality
- Evidence40
- Adoption
- Not scored
- Hype gap+25
- Incentives55
- Confidence40
OpenAI says better caching and inference let it cut API prices for Sol and Luna by half, and the cost advantage it claims for the cheap tier over the old top tier comes in at one tenth on the benchmark it published and one hundredth in its summary.
Perspective Coverage
8 publishers
- Builder
- Builder 36%
- Operator
- Operator 42%
- Investor
- Investor 22%
Reality
- Evidence40
- Adoption
- Not scored
- Hype gap+35
- Incentives70
- Confidence60
OpenAI's 50 percent API price cut doubles the token volume a flat budget buys
OpenAI's own benchmarks put GPT-6 Sol and Luna at half the list price and a fraction of a rival's cost per finished task. For defenders, the thing getting cheaper is autonomous tool calls into business systems.
Reality
- Evidence32
- Adoption38
- Hype gap+34
- Incentives80
- Confidence52
OpenAI cuts prices on new GPT-6 Sol and Luna models
Sol now bills $2 and $10 per million tokens and Luna $0.10 and $0.50, while OpenAI quotes its own benchmark results per task, where the cheap model lands 2.2 points behind Sol on the software engineering test.
Reality
- Evidence42
- Adoption55
- Hype gap+25
- Incentives78
- Confidence52
Sol lists at $2 and $10 per million tokens and Luna at $0.10 and $0.50. The per-task savings OpenAI published come mostly from the lower price, and the cheaper tier scores below its predecessor on computer use.
Reality
- Evidence55
- Adoption32
- Hype gap+30
- Incentives82
- Confidence62
A Berkeley scanning agent scores 100% on five AI agent benchmarks without solving a task
Berkeley's RDI center attacked the step where each benchmark computes its score, and without solving a task its own scorecard reports 100% on five of the eight, about 98% on GAIA and 73% on OSWorld.
Publishers:rdi.berkeley.edu
Reality
- Evidence45
- Adoption35
- Hype gap+20
- Incentives55
- Confidence50
buildOne report1 publisher Anthropic prices its newer Sonnet a third below Sonnet 4.5
Sonnet 4.5 still leads GPT-5 on the coding leaderboards, and GPT-5 lists about 46 percent below it on a 5:1 token mix. Anthropic's current Sonnet undercuts both of Sonnet 4.5's list prices, and that complicates a routing plan built on the older pair.
Reality
- Evidence40
- Adoption20
- Hype gap+15
- Incentives55
- Confidence45
buildOne report1 publisher Mano-CUA's ten-point win over Claude shrinks to 0.8 points against Gemini
Mininglamp published NavEval scores for its own model on its own benchmark. Across the three entries, the spread tracks how each stack reads a page. It is not a case of specialists beating frontier models.
Reality
- Evidence24
- Adoption12
- Hype gap+46
- Incentives86
- Confidence58
buildOne report1 publisher Harness-Bench makes the scaffold a measured variable, and model-only scores lose meaning
A new diagnostic benchmark treats the execution layer as something to vary rather than a fixed backdrop. Its conclusion is that agent capability belongs to a model-harness pair.
Reality
- Evidence42
- Adoption10
- Hype gap+22
- Incentives55
- Confidence40